Learning temporal context for activity recognition

We investigate how incremental learning of long-term human activity patterns improves the accuracy of activity classification over time. Rather than trying to improve the classification methods themselves, we assume that they can take into account prior probabilities of activities occurring at a particular time. We use the classification results to build temporal models that can provide these priors to the classifiers. As our system gradually learns about typical patterns of human activities, the accuracy of activity classification improves, which results in even more accurate priors. Two datasets collected over several months containing hand-annotated activity in residential and office environments were chosen to evaluate the approach. Several types of temporal models were evaluated for each of these datasets. The results indicate that incremental learning of daily routines leads to a significant improvement in activity classification

Automatic Detection of Human Interactions from RGB-D Data for Social Activity Classification

We present a system for temporal detection of social interactions. Many of the works until now have succeeded in recognising activities from clipped videos in datasets, but for robotic applications, it is important to be able to move to more realistic data. For this reason, the proposed approach temporally detects intervals where individual or social activity is occurring. Recognition of human activities is a key feature for analysing the human behaviour. In particular, recognition of social activities is useful to trigger human-robot interactions or to detect situations of potential danger. Based on that, this research has three goals: (1) define a new set of descriptors, which are able to characterise human interactions; (2) develop a computational model to segment temporal intervals with social interaction or individual behaviour; (3) provide a public dataset with RGB-D data with continuous stream of individual activities and social interactions. Results show that the proposed approach attained relevant performance with temporal segmentation of social activities

Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data

Social activity based on body motion is a key feature for non-verbal and physical behavior defined as function for communicative signal and social interaction between individuals. Social activity recognition is important to study human-human communication and also human-robot interaction. Based on that, this research has threefold goals: (1) recognition of social behavior (e.g. human-human interaction) using a probabilistic approach that merges spatio-temporal features from individual bodies and social features from the relationship between two individuals; (2) learn priors based on physical proximity between individuals during an interaction using proxemics theory to feed a probabilistic ensemble of activity classifiers; and (3) provide a public dataset with RGB-D data of social daily activities including risk situations useful to test approaches for assisted living, since this type of dataset is still missing. Results show that using the proposed approach designed to merge features with different semantics and proximity priors improves the classification performance in terms of precision, recall and accuracy when compared with other approaches that employ alternative strategies

Social Activity Recognition on Continuous RGB-D Video Sequences

Modern service robots are provided with one or more sensors, often including RGB-D cameras, to perceive objects and humans in the environment. This paper proposes a new system for the recognition of human social activities from a continuous stream of RGB-D data. Many of the works until now have succeeded in recognising activities from clipped videos in datasets, but for robotic applications it is important to be able to move to more realistic scenarios in which such activities are not manually selected. For this reason, it is useful to detect the time intervals when humans are performing social activities, the recognition of which can contribute to trigger human-robot interactions or to detect situations of potential danger. The main contributions of this research work include a novel system for the recognition of social activities from continuous RGB-D data, combining temporal segmentation and classification, as well as a model for learning the proximity-based priors of the social activities. A new public dataset with RGB-D videos of social and individual activities is also provided and used for evaluating the proposed solutions. The results show the good performance of the system in recognising social activities from continuous RGB-D data

Fuzzy-Based Variable Speed Limits System Under Connected Vehicle Environment: A Simulation-Based Case Study in the City of Naples

This paper handles the problem of controlling speed limits on freeways in a connected traffic environment to reduce traffic congestion and improve both the operational and environmental performance of the road network. In order to achieve this objective, we present a Variable Speed Limit (VSL) system that utilizes fuzzy logic, which adjusts the speed limits that connected vehicles must comply with by leveraging traffic data such as vehicle flow, occupancy, and speed obtained from loop detectors installed along the road. To evaluate the effectiveness of the proposed Fuzzy-based VSL system and its potential benefits compared to the conventional rule-based VSL system in terms of traffic congestion and environmental impact, we conducted a simulation analysis using the microscopic traffic simulator, VISSIM. Specifically, three simulation scenarios are taken into account: i) no VSL, where the VSL system is not enabled; ii) Rule-based VSL system, where a typical a decision tree-based system is considered; iii) Fuzzy-based VSL system, where the herein proposed approach is appraised. The results demonstrate that the proposed approach enhances road efficiency by decreasing speed variation, increasing average speed and vehicle volume, and reducing fuel consumption

Chronic HBV infection in pregnant immigrants: a multicenter study of the Italian Society of Infectious and Tropical Diseases

The aims of the study were to estimate the clinical impact of HBV infection in pregnant immigrants and their family members and to identify a useful approach to managing the healthcare of HBsAg-positive immigrants. Included in this study were 143 HBsAg-positive pregnant immigrants of the 1,970 from countries with intermediate/high HBV endemicity who delivered in 8 Italian hospitals in 2012-2013. In addition, 172 family members of 96 HBsAg-positive pregnant immigrants were tested for serum HBsAg. The median age of the 143 HBsAg-positive pregnant immigrants was 31.0±12.1 years and the length of stay in Italy 5.0±4.1 years; 56.5% were unaware of their HBsAg positivity. HBV DNA was detected in 74.5% of the pregnant immigrants, i.e., 94.3% from Eastern Europe, 72.2% from East Asia and 58.1% from Sub-Saharan Africa. HBV DNA ≥2000 IU/mL was detected in 47.8% of pregnant immigrants, associated with ALT ≥1.5 times the upper normal value in 15% of cases. Anti-HDV was detected in 10% of cases. HBsAg was detected in 31.3% of the 172 family members. All HBsAg-positive immigrants received counseling on HBV infection and its prevention, and underwent a complete clinical evaluation. The findings validate the approach used for the healthcare management of the HBsAg-positive immigrant population

HCV antiviral therapy in injection drug users: difficult to treat or easy to cure?

Background and rationale of the study. Hepatitis C infection is very common among injection drug users(IDUs). In clinical practice there is reluctance to treat IDUs, because considered difficult-to-treat. Aim of this study was to evaluate the response to antiviral treatment in IDUs compared to non-IDUs. Main results. In this observational retrospective study, 204 non cirrhotic patients(112 IDUs, 92 non-IDUs) with chronic hepatitis C, treated with PEG-IFN and ribavirin in a tertiary centre for IDUs of Southern Italy from 2008 to 2011 were analyzed. Age, sex, genotype, steatosis, response to previous therapy, rapid(RVR), early(EVR), end-of-treatment(ETR), sustained(SVR) virological response were evaluated. IDUs were mainly young and males, with prevalence of genotype 3. A higher SVR rate in IDUs group compared to non-IDUs only in PerProtocol(PP) analysis (90% vs. 78,9% ;p = 0.04). On the contrary, in IntentionToTreat(ITT) analysis, no significant difference was relieved. A higher SVR rate at ITT analyses in naive non-IDUs patients was found (76,13% vs. 90%, p = 0.021), but at PP analysis wasn't confirmed. Treatment was well tolerated; a higher dropout rate was reported in IDUs (24 patients) compared to non-IDUs (2 patients). In order to exclude the effect of viral genotypes on SVR a genotype matched statistical analysis was done and no difference was found. Conclusions. IDUs naive patients, due to young age and high prevalence of genotype 3, appear good candidates to dual antiviral therapy with high SVR rates. Dropout is the main non-response cause among these subjects, but through an optimal monitoring program with a multidisciplinary setting, their "difficult to treat" characteristics can be overcome

Management of QT prolongation induced by anti-cancer drugs: Target therapy and old agents. Different algorithms for different drugs

The side effects of anticancer drugs still play a critical role in survival and quality of life. Although the recent progresses of cancer therapies have significantly improved the prognosis of oncologic patients, side effects of antineoplastic treatments are still responsible for the increased mortality of cancer survivors. Cardiovascular toxicity is the most dangerous adverse effect induced by anticancer therapies. A survey conducted by the National Health and Nutrition Examination, showed that 1807 cancer survivors followed up for seven years: 51% died of cancer and 33% of heart disease (Vejpongsa and Yeh, 2014). Moreover, the risk of cardiotoxicity persists even with the targeted therapy, the newer type of cancer treatment, due to the presence of on-target and off-target effects related to this new class of drugs. The potential cardiovascular toxicity of anticancer agents includes: QT prolongation, arrhythmias, myocardial ischemia, stroke, hypertension (HTN), thromboembolism, left ventricular dysfunction and heart failure (HF). Compared to other cardiovascular disorders, the interest in QT prolongation and its complications is fairly recent. However, oncologists have to deal with it and to evaluate the risk-benefit ratio before starting the treatment or during the same. Electrolyte abnormalities, low levels of serum potassium and several drugs may favour the acquired QT prolongation. Treatment of marked QT prolongation includes cardiac monitoring, caution in the use or suspension of cancer drugs and correction of electrolyte abnormalities (hypokalaemia, hypomagnesaemia, hypocalcaemia). Syndrome of QT prolongation can be associated with potentially fatal cardiac arrhythmias and its treatment consists of intravenous administration of magnesium sulphate and the use of electrical cardioversion

Release of Polycyclic Aromatic Hydrocarbons and Heavy Metals from Rubber Crumb in Synthetic Turf Fields: Preliminary Hazard Assessment for Athletes

Synthetic turf, made with an infill of rubber crumb from used tyres or virgin rubber, is now common in many sporting facilities. It is known that it contains compounds such as polycyclic aromatic hydrocarbons (PAHs) and heavy metals. We evaluated in nine samples of rubber crumb the total content of some heavy metals (Zn, Cd, Pb, Cu, Cr, Ni, Fe) normally found in tyres by microwave mineralization and the levels of the 14 US EPA priority PAHs by Soxhlet extraction and HPLC analysis. The results showed high levels of PAHs and zinc in all rubber crumb samples compared to rubber granulate limits set by Italian National Amateur League (LND). Following the precautionary principle, a risk assessment at 25°C was done, using the Average Daily Dose (ADD) assumed by athletes, expressed in terms of mass of contaminant per unit of body weight per day (mg/kg day), and the Lifetime Average Daily Dose (LADD) and then evaluating the Hazard Index (HI) and the Cumulative Excess Cancer Risk (∑ECR). In the different rubber granulates samples the HI ranges from a minimum of 8.94×10-7 to a maximum of 1.16×10-6, while the ∑ECR ranges from a minimum of 4.91×10-9 to a maximum of 1.10×10-8. Finally, the aim of this study was to estimate the “hazard” for athletes inhaling PAHs released at the high temperatures this synthetic turf may reach. Then a sequence of proofs was carried out at 60°C, a temperature that this rubber crumb can easily reach in sporting installations, to see whether PAH release occurs. The toxicity equivalent (TEQ) of eva